{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自动控制理论  \n",
    "---  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 我以前一直很讨厌`python`的语法，但是她庞大的三方库又让一切都那么方便。hmm，现在开始有些喜欢上她了，尤其是`.ipynb`这种笔记文件，简直太好用了。  \n",
    " \n",
    "最近在重新学习自动控制理论，当工作了六年之后，回头再去看大学的课程别有一番体会。也许学习从来不是一遍就能做好的，理工科的知识似乎更需要咬文嚼字，因为绝大多数知识都是由使用范围的。在现在计算机技术的加持下，我以前纠结的、不擅长的东西（尤其是绘根轨迹图），似乎都不是什么问题了。一瞬间感觉自己似乎多出了更多的精力和时间。就像芙所说的那样：“你得把你有限的时间创造更多的意义和价值，而不是走前人走过的老路”。也许这不是能考高分的做法，但这应该是比较适合科研、学习和工作的思路。我们要学着相信别人，然后走上更高的高度。(ง •_•)ง  \n",
    " \n",
    "我很佩服会整理知识的人，也很乐意去整理知识。当下而言，更有用的是如何发现别人已经整理好的知识。似乎很多东西都有一个`Cheatsheet`。😄"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cheatsheet  \n",
    "> 好记性不如烂笔头。所以不要在意究竟是把知识记在心里还是记在纸上，重要的是你要会用就好了！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jupyter 相关  \n",
    "在`.ipynb` 中执行命令，`cmd`、`bash`或者`shell`。可以在`Python`代码框内以`!`加命令实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 安装本系列中需要用到的python 库\n",
    "!python -m pip install sympy control matplotlib tbcontrol\n",
    "\n",
    "# 调用系统的echo 命令\n",
    "!echo done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib`绘图，运行一次`%matplotlib inline`之后用`matplotlib`作图不需要`plt.show()`了就"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "# 运行一次之后就在用matplotlib 作图就不需要plt.show() 了\n",
    "\n",
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3]);  # Plot some data on the axes.\n",
    "\n",
    "\n",
    "# %run file.py\n",
    "# 运行python 脚本  "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
